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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: peft |
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--- |
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## Usage |
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Here is an example of how you would load: |
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```python |
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import torch |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("mwz/zephyr-khaadi") |
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inputs = tokenizer(inp_str, return_tensors="pt").to("cuda") |
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model = AutoPeftModelForCausalLM.from_pretrained( |
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"mwz/zephyr-khaadi", |
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low_cpu_mem_usage=True, |
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return_dict=True, |
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torch_dtype=torch.float16, |
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device_map="cuda") |
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generation_config = GenerationConfig( |
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do_sample=True, |
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top_k=1, |
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temperature=0.1, |
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max_new_tokens=150, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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def process_data_sample(messages): |
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processed_example = "" |
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for message in messages: |
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role = message["role"] |
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content = message["content"] |
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processed_example += f"<|"+role+"|>\n "+content+"\n" |
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return processed_example |
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``` |
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Inference can then be performed as usual with HF models as follows: |
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```python |
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messages = [ |
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{"role": "system", "content": "You are a Khaadi Social Media Post Generator who helps with user queries or generate him khaadi posts give only three hashtags and be concise as possible dont try to make up."}, |
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{"role": "user", "content": "Generate post on new arrival of winter"}, |
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] |
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inp_str = process_data_sample(messages) |
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inputs = tokenizer(inp_str, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, generation_config=generation_config) |
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asnwer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(asnwer) |
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``` |
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Expected output similar to the following: |
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``` |
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<|system|> |
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You are a Khaadi Social Media Post Generator who helps with user queries or generate him khaadi posts give only three hashtags and be concise as possible dont try to make up. |
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<|user|> |
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Generate post on new arrival of winter |
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#Khaadi #WinterArrivals #Winter21 |
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Winter is here and we’ve got you covered! |
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Available in-stores and online |
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#Khaadi #WinterCollection #Winter2024 #WinterArrivals #Khaadi #KhaadiFabrics #KhaadiHome |
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``` |
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